Rishab Jeevan

I’m an AI Engineer who ships LLM-powered applications, AI agents, and automation systems using Python, FastAPI, OpenAI API, and Anthropic API. I build AI-native products that solve real user problems through self-directed research into the latest models, agent frameworks, and retrieval systems.

I recently built and deployed a voice AI agent end-to-end—covering agent architecture, memory systems, prompt engineering, RAG research, and backend integration. In Ather AI, I engineered real-time STT/TTS pipelines with Whisper and FastAPI, using prompt compression and response streaming optimizations to achieve sub-second voice interactions.

I designed agentic workflows with prompt chaining, tool-use orchestration, and few-shot prompt engineering techniques to improve task accuracy across multi-step automation scenarios. I also independently scoped, built, and shipped the full MVP within 3 weeks—owning ideation, backend API integration, conversational UX design, and cloud deployment on Render.

I’m continuing that same focus on retrieval-grounded behavior with my Geospatial Real Estate Valuation Engine in development. I’m implementing a RAG-inspired retrieval layer to ground valuation outputs in live comparable-record data, with the goal of reducing hallucination risk in price estimates.